Senior Developer

Job Summary

We are seeking an experienced Senior AI/ML Engineer to lead the design and development of enterprise-grade AI solutions, including production RAG systems, classical ML models, and deep learning pipelines.

Must-have Skills :Artificial Intelligence, Machine Learning, C#
Degree :B.Tech/B.E.

Key Responsibilities

Responsibilities

Architect and build Enterprise RAG systems — chunking strategies, retrieval pipelines, re-ranking, evaluation, and guardrails at scale
Design and implement ML models for business problems — feature engineering, model selection, training, evaluation, and deployment
Build and optimize XGBoost models for structured/tabular data use cases (classification, regression, ranking)
Develop LSTM (Long Short-Term Memory) and other neural network architectures for sequential/time-series data
Train and deploy models using PyTorch and TensorFlow
Design advanced LangChain & LangGraph workflows — multi-agent systems, tool use, routing, memory management
Architect Vector Database solutions — index design, hybrid search, scaling, and performance tuning
Mentor junior engineers and drive technical standards across the team
Evaluate and integrate new AI/ML tools and frameworks
Required Skills

Skill Requirements

5+ years of experience in software engineering with 3+ years focused on ML/AI
Strong ML fundamentals — supervised/unsupervised learning, model evaluation, hyperparameter tuning, cross-validation, bias-variance tradeoff
Production experience with Enterprise RAG — document ingestion pipelines, chunking strategies, semantic search, re-ranking (Cohere, cross-encoders), evaluation , and hallucination mitigation
Experience with XGBoost — feature importance, handling imbalanced data, model interpretability (SHAP)
Deep learning expertise — LSTM, RNN, attention mechanisms, sequence-to-sequence models
Proficiency in PyTorch and/or TensorFlow for model development and training
Advanced usage of LangChain & LangGraph — custom agents, graph-based orchestration, streaming, fallback handling
Strong Vector Database expertise — schema design, ANN algorithms (HNSW, IVF), hybrid search (dense + sparse), scaling strategies
Experience deploying ML models to production (model serving, monitoring, drift detection)
Nice to Have

Experience with MLOps tools (MLflow, Weights & Biases, Kubeflow)
Knowledge of transformer architectures
Cloud ML services (Azure ML, SageMaker, Vertex AI)

Other Requirements

Job Description:
About the Role

We are seeking an experienced Senior AI/ML Engineer to lead the design and development of enterprise-grade AI solutions, including production RAG systems, classical ML models, and deep learning pipelines.

Responsibilities

Architect and build Enterprise RAG systems — chunking strategies, retrieval pipelines, re-ranking, evaluation, and guardrails at scale
Design and implement ML models for business problems — feature engineering, model selection, training, evaluation, and deployment
Build and optimize XGBoost models for structured/tabular data use cases (classification, regression, ranking)
Develop LSTM (Long Short-Term Memory) and other neural network architectures for sequential/time-series data
Train and deploy models using PyTorch and TensorFlow
Design advanced LangChain & LangGraph workflows — multi-agent systems, tool use, routing, memory management
Architect Vector Database solutions — index design, hybrid search, scaling, and performance tuning
Mentor junior engineers and drive technical standards across the team
Evaluate and integrate new AI/ML tools and frameworks
Required Skills

5+ years of experience in software engineering with 3+ years focused on ML/AI
Strong ML fundamentals — supervised/unsupervised learning, model evaluation, hyperparameter tuning, cross-validation, bias-variance tradeoff
Production experience with Enterprise RAG — document ingestion pipelines, chunking strategies, semantic search, re-ranking (Cohere, cross-encoders), evaluation , and hallucination mitigation
Experience with XGBoost — feature importance, handling imbalanced data, model interpretability (SHAP)
Deep learning expertise — LSTM, RNN, attention mechanisms, sequence-to-sequence models
Proficiency in PyTorch and/or TensorFlow for model development and training
Advanced usage of LangChain & LangGraph — custom agents, graph-based orchestration, streaming, fallback handling
Strong Vector Database expertise — schema design, ANN algorithms (HNSW, IVF), hybrid search (dense + sparse), scaling strategies
Experience deploying ML models to production (model serving, monitoring, drift detection)
Nice to Have

Experience with MLOps tools (MLflow, Weights & Biases, Kubeflow)
Knowledge of transformer architectures
Cloud ML services (Azure ML, SageMaker, Vertex AI)